방법 비교

	온라인 로지스틱 회귀	정규화 로지스틱 회귀
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1960s (perceptron); formalized for logistic loss ~2000s	1996–2005
창시자	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
유형	Incremental supervised classifier	Penalized classification model
원전	Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
별칭	incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
관련	5	5
요약	Online Logistic Regression fits a logistic classifier one sample (or mini-batch) at a time via stochastic gradient descent, updating model weights as each observation arrives rather than waiting to see the full dataset. This makes it the standard choice for high-volume, streaming, or memory-constrained binary classification problems where batch training is infeasible.	Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.